International Journal of Communication Systems最新文献

In this article, a two-port multiple-input multiple-output (MIMO) hybrid rectangular dielectric resonator antenna (DRA) with machine learning (ML) approach for the n261 5G New Radio (NR) application is presented. The proposed antenna is designed on an RT/duroid 5880 (Ɛr = 2.2) substrate activated by 50 Ω, L-shaped microstrip slot feeds beneath both DRAs. The isolation is more than 19 dB, and the gain is 10 dBi in the operating frequency range. The proposed antenna is optimized through knowledge-based neural networks (KBNN), artificial neural networks (ANNs), and ML. The optimal design parameters of the proposed antenna are accomplished using the ML optimization approach, which includes ridge regression, ANNs, and KBNN. KBNN ML techniques provide 96.88% accuracy and correctly predict the S-parameters of the proposed antenna. The MIMO diversity parameters like envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), and channel capacity loss (CCL) are calculated and found within the limits. Hence, the proposed antenna is used for 5G NR mm-wave application.

For the sensor network, greedy forwarding is thought to be the best energy-efficient routing method. There are two restrictions on greedy forwarding: a minimum local scenario and an intersection problem in which it is unable to locate an existing route. The proposed independent gradient forward (IGF) proves that the Euclidean equation used by greedy forwarding is less capable of dealing with the problems mentioned above. IGF considers each dimension independently to enhance the efficiency of greedy forwarding, sustaining its simplicity. It contributes to strengthening the suggested IGF's ability to manage holes and get around obstructions in the wireless sensor network. We have demonstrated that it travels a shorter potential path and avoids stuck nodes well in advance. It is more energy-efficient since it can predict the void node and never visits a stalled node. We evaluated the suggested IGF's performance against other traditional boundary traversal-based methods on both rare and dense networks. In comparison with other boundary traversal-based algorithms, the simulation results indicate that it generates a 10% smaller route to bypass. The IGF average energy consumption increases slowly, rising by just 7% and 12.5% for dense and rare networks, respectively, with a fivefold increase in void size (from 100 to 500 m).

Wireless body area networks (WBANs) are essential for medical applications, especially in remote health monitoring, as they transmit crucial and time-sensitive data collected by nodes positioned around or within the body. However, the coexistence of WBANs with wireless channels can degrade performance due to interference. This study introduces OIM-DLCAM, an optimal interference mitigation scheme for WBANs, which utilizes a deep learning-based channel access method. OIM-DLCAM addresses interference through the multiobjective Hungarian optimization (MOHO) algorithm, considering design constraints such as node transmission power, packet delivery ratio, and interference range. Additionally, it employs a deep probabilistic neural network-based channel access method (DPNN-CAM) to effectively mitigate interference by making decisions regarding contention window size, frame length, and buffer size. The proposed OIM-DLCAM scheme ensures fairness between users while enhancing system performance. Simulation results from both static and dynamic sensor node scenarios demonstrate its effectiveness under various conditions, showcasing its potential to improve WBAN performance in medical applications. The simulations reveal that OIM-DLCAM outperforms existing state-of-the-art schemes across various scenarios, with efficiency gains of up to 86.187%, 72.452%, and 47.954% for WBAN node density, mobility, and packet arrival rate, respectively. Moreover, it significantly reduces the average end-to-end delay and packet drop rate while improving throughput and packet delivery ratio compared with existing schemes. Additionally, comparisons with industry standards, such as the IEEE 802.15.4e norm, validate the suitability of OIM-DLCAM for cofounded WBANs.

A simple planar wideband four-port MIMO antenna of size 2.75𝜆_l × 2.75𝜆_l × 0.04𝜆_l (𝜆_l is the wavelength when f_l is 24.26 GHz) with high isolation that offers the 24.26 to 30.2 GHz bandwidth covering the 5G FR-2 spectrums is proposed in this work. Total fractional bandwidth of 21.8% has been achieved that covers the n257 (26.5 to 29.5 GHz), n258 (24.25 to 27.5 GHz), and n261 (27.5 to 28.35 GHz) 5G spectrums. Popular rectangular radiator-based 4-port MIMO antenna with symmetric stubs loaded parallel to feed line and tapered semi-circular cut in the lower corners of patch is incorporated that exhibits an excellent isolation that is below −27 dB through the entire spectrum and the extreme realized separation is −40 dB. The projected MIMO radiator is premeditated on RT/duroid 5880 of 0.508 mm depth and permittivity (ε_r) of 2.2. The maximum radiation efficiency and gain of the proposed antenna are 93.07% and 6.48 dBi, respectively. The MIMO antenna parameters such as envelope correlation coefficient (ECC) were achieved lesser than 0.003, diversity gain (DG) was achieved around 10 dB, channel capacity loss (CCL) was achieved lower than 0.15 bits/s/Hz, and mean effective gain (MEG) was achieved between −3.6 dB and −3 dB throughout the impedance band, which exhibit the excellent MIMO properties, and thus, the projected antenna is appropriate for 5G/beyond 5G wireless technologies.

This research work presents a miniaturized enhanced angular and polarization stability frequency selective surface (FSS) structure at 660 MHz for electromagnetic (EM) shielding of 4G LTE networks. The vias and spiral-shaped subunit cells are combined to extend the electrical path length corresponding to the lower resonant frequency. The symmetric structure is considered to improve polarization and angular stability. Two symmetric metallic layers are etched on both sides of the FR4 substrate with thickness $�h�=�1.6��\mathrm{mm}$ and relative permittivity $�{\epsilon }_r�=�4.4$. Four steps are adopted to improve miniaturization and stability (angular and polarization). The dimension of the unit cell is $�{\left(�0.016�{\lambda }_0�\right)}^2$. The figure of merit is also improved to $�\frac{{\lambda }_0}{P}�=�59.49$, where $�{\lambda }_0$ is the largest free space wavelength and P is the periodicity of the proposed structure. The proposed band-stop FSS is more angularly stable as a figure of merit for various incidence angles under transverse electric (TE) and transverse magnetic (TM) polarizations. Simulation and measurement have been done to confirm the polarization insensitivity and maximum angular stability of 80°.

The Internet of Vehicles (IoV) is a hot research topic in intelligent transportation, and the research on new methods of intelligent data transmission is one of the key contents. Aiming at the application scenario of urban air quality monitoring data collection and transmission based on IoV, a new reliable data transmission method (Vehicular Grouping-Communicated Data [VGCD]) based on crowd sensing strategy is proposed. This method is designed to utilize the intelligent perception and collaborative monitoring of vehicle to collect data, avoiding redundancy and overload of information; a reliable data transmission minimum delay hybrid routing method is proposed in the data transmission part, which combines encoding mechanism with routing design, integrates routing switching ideas, predicts vehicle adaptive connectivity based on fuzzy logic, and makes probability based routing decisions to minimize delay, achieving reliable and efficient data collection and transmission. The proposed new method has important theoretical significance and practical value for various applications such as dynamic remote perception monitoring based on the IoV in intelligent transportation.

The increasing prevalence of MIMO technology in wireless communication networks is attributed to its ability to augment system capacity and dependability. Consequently, there is a notable enthusiasm for the advancement and implementation of S-band applications. Nevertheless, the mutual coupling between numerous antennas presents a crucial obstacle to MIMO system performance. This paper presents a novel technique that utilizes a defective ground structure with parasitic components to increase antenna isolation and gain. Additionally, an L-shaped slot is incorporated into the patch element to enhance the antennas impedance bandwidth. Antennas are intentionally placed in a perpendicular arrangement to minimize the impact of the coupling effects. The antenna exhibits an impedance bandwidth in the frequency range of 2.62–2.79 GHz. Furthermore, the utilization of a four-element MIMO antenna setup with a defective ground structure yields outstanding isolation properties, with values notably lower than −25 dB. To achieve an optimal MIMO performance, we assessed various diversity parameters, including diversity gain, channel capacity loss, total active reflection coefficient, and envelope correlation coefficient. The parameter values were within acceptable limits (ECC < 0.04, DG = 10 dB, TARC < 0 dB, and CCL < 0.1 bits/s/Hz). This research paper therefore offers essential insights into the design and optimization of MIMO antennas specifically for 2.7 GHz applications.

The Internet of Things (IoT) continues to find novel applications across various domains. These applications require the support of efficient communication protocols in the Low Power and Lossy Networks (LLNs). The development of efficient multicast protocols is one such area that requires critical research attention. Efficient multicast protocols are especially important in the non-storing mode of IPv6 Routing Protocol for LLNs (RPL), considering its applicability in real-world IoT applications. However, the development of non-storing RPL multicast protocols needs to handle the intricacies associated with source routing. Addressing these issues, this paper presents a novel multicast protocol named Multicast Forwarding in LLNs under Non-storing mode (MFLN). MFLN uses Bloom Filters to minimize the overhead that comes with source routing. While retaining the best features of multicast protocols operating in the storing mode of RPL such as bidirectional traffic and cross-layer optimization with ContikiMAC, MFLN also includes a novel metric named Mode Selection Metric (MSM) to decide the suitable link-layer forwarding method. MSM makes this decision based on the ability of a node, in terms of its residual energy, to perform multiple link-layer unicasts. Thus, MFLN saves the node battery from running out and at the same time reduces the duplicate packets created due to link-layer broadcasts. The simulations conducted using Contiki-COOJA indicate considerable improvements in terms of packet delivery ratio, energy consumption per delivered packet, packet transmissions, end-to-end delay, and normalized routing overhead for the proposed protocol.

Radio frequency (RF) energy harvesting (EH) for wireless networks provides a green and sustainable solution and offers an energy-efficient system. It is most crucial for fulfilling the power requirement of the rising number of low-powered wireless devices and achieving sustainable development goals (SDG). It ensures the longevity of devices in the network even at inaccessible remote locations. Such RF-EH-based mechanisms are utilized in simultaneous wireless information and power transfer (SWIPT) systems. Most of the prevailing studies have analyzed the SWIPT system over conventional fading channels. However, due to the higher rate requirements, the current paradigm shift in frequency usage shifts towards the GHz band, which also offers better EH efficiency. Hence, SWIPT system analysis over the channel that is suitable for mm-wave signal propagation is dealt with in this study. This study evaluates SWIPT performance over fluctuating two-ray (FTR) fading channel that is crucial since it provides the best fit for characterizing GHz signal and also provides a generalized framework for most of the conventional fading channels. This study presents new analytical results assuming time switch relaying (TSR) protocol with (i) amplify and forward (AF) and (ii) decode and forward (DF) relaying. The results are also analyzed for H branches maximal ratio combining (MRC) diversity technique and evaluated the diversity gain for both AF and DF relays. The effectiveness of the system is measured in terms of system outage probability (SOP) to envisage the reliability of SWIPT based system. This work presents the simulation and modeling of such system and its obtained results are validated by Monte Carlo simulations for their accuracy.

Deploying a wireless sensor network (WSN) for accessing the network of distant environments is more crucial. Node defect detection is a core technology of WSN and is essential for most WSN networks. The defective node reduces the overall service quality (OSQ) of the global WSN network system. Therefore, a type II fuzzy inference system (T2_FIS) is proposed for detecting defective nodes in the WSN network. The adaptive genetic algorithm (AGA) and proposed method are introduced for tuning the parameters in the T2_FIS system. The proposed T2_FIS method effectively detects the broken node in the WSN network using the fuzzy rules. In addition, the improved Mud Ring (IMR) optimization algorithm is proposed to replace the faulty node with the neighborhood node in the network. The defective nodes are identified and replaced with the closest nodes based on the membership function (MF) generated by the T2_FIS. Furthermore, the lifetime and throughput of the WSN network are increased by minimizing energy consumption. The overall performance is evaluated using the MATLAB tool, and the implementation results are compared with the existing methods. The total energy consumption for the proposed method is 50.451, with a throughput and lifetime of 153.657 and 22979.25. Furthermore, the performance metrics for the existing and proposed strategies are analyzed, and the accuracy of the proposed method is proven to be 99.3827%, the false alarm rate (FAR) is 0.0008, and the false positive rate (FPR) is 0.0617. The results show the effectiveness and performance of the proposed method.